- A
Labelling training data with semantic tags for custom NLP model training
Why wrong: Annotation tagging is data labelling — SRL is a specific NLP analysis identifying who does what to whom in a sentence.
- B
Identifying who did what to whom, when, and where by assigning roles to sentence participants
SRL extracts predicate-argument structure — 'John (Agent) gave (predicate) Mary (Recipient) the book (Theme)' — enabling deeper language understanding.
- C
Classifying the semantic category (positive, negative, neutral) of each sentence role
Why wrong: Sentiment per sentence is sentiment analysis — SRL assigns grammatical-semantic roles (Agent, Patient, Location) to participants.
- D
Assigning roles and responsibilities to team members in an AI project
Why wrong: Project management is team coordination — semantic role labelling is an NLP analysis technique for sentence structure.
Quick Answer
The correct answer is that semantic role labeling identifies who did what to whom, when, and where by assigning roles like agent, patient, and location to sentence participants. This is correct because SRL goes beyond simple keyword matching to uncover the predicate-argument structure, enabling a machine to understand the underlying meaning of a sentence rather than just its surface words. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your grasp of how Azure AI Language Understanding services, such as LUIS and Text Analytics, achieve deeper comprehension for tasks like question answering and event detection. A common trap is confusing SRL with named entity recognition (NER), which only tags entities like names or dates without showing their relational roles. Remember the memory tip: SRL answers the journalist’s questions—Who? Did What? To Whom? When? Where?—making it the backbone of true language understanding.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is 'semantic role labelling' (SRL) and why is it important for language understanding?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Identifying who did what to whom, when, and where by assigning roles to sentence participants
Semantic role labeling (SRL) is a natural language processing technique that identifies the predicate-argument structure in a sentence, assigning roles such as agent, patient, instrument, location, and time to sentence constituents. It is important for language understanding because it enables a machine to answer 'who did what to whom, when, and where,' which is foundational for tasks like information extraction, question answering, and event detection. In Azure AI services, SRL is a capability within the Language Understanding (LUIS) and Text Analytics APIs that helps build deeper comprehension of text beyond simple keyword matching.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Labelling training data with semantic tags for custom NLP model training
Why it's wrong here
Annotation tagging is data labelling — SRL is a specific NLP analysis identifying who does what to whom in a sentence.
- ✓
Identifying who did what to whom, when, and where by assigning roles to sentence participants
Why this is correct
SRL extracts predicate-argument structure — 'John (Agent) gave (predicate) Mary (Recipient) the book (Theme)' — enabling deeper language understanding.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Classifying the semantic category (positive, negative, neutral) of each sentence role
Why it's wrong here
Sentiment per sentence is sentiment analysis — SRL assigns grammatical-semantic roles (Agent, Patient, Location) to participants.
- ✗
Assigning roles and responsibilities to team members in an AI project
Why it's wrong here
Project management is team coordination — semantic role labelling is an NLP analysis technique for sentence structure.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse semantic role labeling with sentiment analysis or general data labeling, because the word 'semantic' and 'role' are misleadingly broad, but the exam specifically tests the linguistic definition of identifying predicate-argument structures.
Detailed technical explanation
How to think about this question
Under the hood, SRL typically uses a BIO tagging scheme (Begin, Inside, Outside) to label spans of tokens with roles like B-ARG0 (agent) or I-ARG1 (patient), often leveraging a bidirectional LSTM or transformer model fine-tuned on PropBank or FrameNet corpora. A subtle behavior is that SRL must handle null instantiations (implicit arguments) and coreference resolution to correctly assign roles when a participant is omitted or referred to by a pronoun. In a real-world scenario, an Azure-based customer support chatbot uses SRL to extract the product (patient) and the issue (predicate) from a user query like 'My laptop won't charge,' enabling precise troubleshooting without requiring exact keyword matches.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Identifying who did what to whom, when, and where by assigning roles to sentence participants — Semantic role labeling (SRL) is a natural language processing technique that identifies the predicate-argument structure in a sentence, assigning roles such as agent, patient, instrument, location, and time to sentence constituents. It is important for language understanding because it enables a machine to answer 'who did what to whom, when, and where,' which is foundational for tasks like information extraction, question answering, and event detection. In Azure AI services, SRL is a capability within the Language Understanding (LUIS) and Text Analytics APIs that helps build deeper comprehension of text beyond simple keyword matching.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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